Andreas Ciroth

Framework for modelling data uncertainty in life cycle inventories

Modelling data uncertainty is not common practice in life cycle inventories (LCI), although different techniques are available for estimating and expressing uncertainties, and for propagating the uncertainties to the final model results. To clarify and stimulate the use of data uncertainty assessments in common LCI practice, the SETAC working group ‘Data Availability and Quality’ presents a framework for data uncertainty assessment in LCI. Data uncertainty is divided in two categories: (1) lack of data, further specified as complete lack of data (data gaps) and a lack of representative data, and (2) data inaccuracy. Filling data gaps can be done by input-output modelling, using information for similar products or the main ingredients of a product, and applying the law of mass conservation. Lack of temporal, geographical and further technological correlation between the data used and needed may be accounted for by applying uncertainty factors to the non-representative data. Stochastic modelling, which can be performed by Monte Carlo simulation, is a promising technique to deal with data inaccuracy in LCIs.

Uncertainty calculation in life cycle assessments

Goal and BackgroundUncertainty is commonly not taken into account in LCA studies, which downgrades their usability for decision support. One often stated reason is a lack of method. The aim of this paper is to develop a method for calculating the uncertainty propagation in LCAs in a fast and reliable manner.ApproachThe method is developed in a model that reflects the calculation of an LCA. For calculating the uncertainty, the model combines approximation formulas and Monte Carlo Simulation. It is based on virtual data that distinguishes true values and random errors or uncertainty, and that hence allows one to compare the performance of error propagation formulas and simulation results. The model is developed for a linear chain of processes, but extensions for covering also branched and looped product systems are made and described.ResultsThe paper proposes a combined use of approximation formulas and Monte Carlo simulation for calculating uncertainty in LCAs, developed primarily for the sequential approach. During the calculation, a parameter observation controls the performance of the approximation formulas. Quantitative threshold values are given in the paper. The combination thus transcends drawbacks of simulation and approximation.Conclusions and OutlookThe uncertainty question is a true jigsaw puzzle for LCAs and the method presented in this paper may serve as one piece in solving it. It may thus foster a sound use of uncertainty assessment in LCAs. Analysing a proper management of the input uncertainty, taking into account suitable sampling and estimation techniques; using the approach for real case studies, implementing it in LCA software for automatically applying the proposed combined uncertainty model and, on the other hand, investigating about how people do decide, and should decide, when their decision relies on explicitly uncertain LCA outcomes-these all are neighbouring puzzle pieces inviting to further work.

Empirically based uncertainty factors for the pedigree matrix in ecoinvent

PurposeEcoinvent applies a method for estimation of default standard deviations for flow data from characteristics of these flows and the respective processes that are turned into uncertainty factors in a pedigree matrix, starting from qualitative assessments. The uncertainty factors are aggregated to the standard deviation. This approach allows calculating uncertainties for all flows in the ecoinvent database. In ecoinvent 2 the uncertainty factors were provided based on expert judgment, without (documented) empirical foundation. This paper presents (1) a procedure to obtain an empirical foundation for the uncertainty factors that are used in the pedigree approach and (2) a proposal for new uncertainty factors, received by applying the developed procedure. Both the factors and the procedure are a result of a first phase of an ecoinvent project to refine the pedigree matrix approach. A separate paper in the same edition, also the result of the aforementioned project, deals with extending the developed approach to other probability distributions than lognormal (Muller et al.).MethodsUncertainty is defined here simply as geometric standard deviation (GSD) of intermediate and elementary exchanges at the unit process level. This fits to the lognormal probability distribution that is assumed as default in ecoinvent 2, and helps to overcome scaling effects in the analysed data. In order to provide the required empirical basis, a broad portfolio of data sources is analysed; it is especially important to consider sources outside of the ecoinvent database to avoid circular reasoning. The ecoinvent pedigree matrix from version 2 is taken as a starting point, skipping the indicator “sample size” since it will not be used in ecoinvent 3. This leads to a pedigree matrix with five data quality indicators, each having five score values. The analysis is conducted as follows: for each matrix indicator and for each data source, indicator scores are set in relation to data sets, building groups of data sets that represent the different data quality indicator scores in the pedigree matrix. The uncertainty in each of the groups is calculated. The uncertainty obtained for the group with the ideal indicator score is set as a reference, and uncertainties for the other groups are set in relation to this reference uncertainty. The obtained ratio will be different from 1, it represents the unexplained uncertainty, additional uncertainty due to a lower data quality, and can be directly used as uncertainty factor candidates.Results and discussionThe developed approach was able to derive empirically based uncertainty factor candidates for the pedigree matrix in ecoinvent. Uncertainty factors were obtained for all data quality indicators and for almost all indicator scores in the matrix. The factors are the result of the first analysis of several data sources, further analyses and discussions should be used to strengthen their empirical basis. As a consequence, the provided uncertainty factors can change in future. Finally, a few of the qualitative score descriptions in the pedigree matrix left room for interpretation, making their application not ambiguous.Conclusions and perspectivesAn empirical foundation for the uncertainty factors in the pedigree matrix overcomes one main argument against their use, which in turn strengthens the whole pedigree approach for quantitative uncertainty assessment in ecoinvent. This paper provides an approach to obtain an empirical basis for the uncertainty factors, and it provides also empirically based uncertainty factors, for indicator scores in the pedigree matrix. Basic uncertainty factors are not provided, it is recommended to use the factors from ecoinvent 2 for the time being. In the developed procedure, using GSD as the uncertainty measure is essential to overcome scaling effects; it should therefore also be used if the analysed data do not follow a lognormal distribution. As a consequence, uncertainty factors obtained as GSD ratios need to be translated to range estimators relevant for these other distributions. Formulas for this step are provided in a separate paper (Muller et al.). The work presented in this paper could be the starting point for a much broader study to provide a better basis for input uncertainty in LCA, not only in ecoinvent.

The application of the pedigree approach to the distributions foreseen in ecoinvent v3

PurposeData used in life cycle inventories are uncertain (Ciroth et al. Int J Life Cycle Assess 9(4):216–226, 2004). The ecoinvent LCI database considers uncertainty on exchange values. The default approach applied to quantify uncertainty in ecoinvent is a semi-quantitative approach based on the use of a pedigree matrix; it considers two types of uncertainties: the basic uncertainty (the epistemic error) and the additional uncertainty (the uncertainty due to using imperfect data). This approach as implemented in ecoinvent v2 has several weaknesses or limitations, one being that uncertainty is always considered as following a lognormal distribution. The aim of this paper is to show how ecoinvent v3 will apply this approach to all types of distributions allowed by the ecoSpold v2 data format.MethodsA new methodology was developed to apply the semi-quantitative approach to distributions other than the lognormal. This methodology and the consequent formulas were based on (1) how the basic and the additional uncertainties are combined for the lognormal distribution and on (2) the links between the lognormal and the normal distributions. These two points are summarized in four principles. In order to test the robustness of the proposed approach, the resulting parameters for all probability density functions (PDFs) are tested with those obtained through a Monte Carlo simulation. This comparison will validate the proposed approach.Results and discussionIn order to combine the basic and the additional uncertainties for the considered distributions, the coefficient of variation (CV) is used as a relative measure of dispersion. Formulas to express the definition parameters for each distribution modeling a flow with its total uncertainty are given. The obtained results are illustrated with default values; they agree with the results obtained through the Monte Carlo simulation. Some limitations of the proposed approach are cited.ConclusionsProviding formulas to apply the semi-quantitative pedigree approach to distributions other than the lognormal will allow the life cycle assessment (LCA) practitioner to select the appropriate distribution to model a datum with its total uncertainty. These data variability definition technique can be applied on all flow exchanges and also on parameters which play an important role in ecoinvent v3.

Geographical and technological differences in life cycle inventories shown by the use of process models for waste incinerators part I. technological and geographical differences

Goal and BackgroundGeographical and technological differences in Life Cycle Inventory data are an important source for uncertainty in the result of Life Cycle Assessments. Knowledge on their impact on the result of an LCA is scarce, and also knowledge on how to manage them in an LCA case study.ObjectiveGoal of this paper is to explore these differences for municipal solid waste incinerator plants, and to develop recommendations for managing technological and geographical differences.MethodologyThe paper provides a definition of technological and geographical differences, and analyses their possible impacts. In a case study, the differences are caused intentionally in ‘games’, by virtually transplanting incineration plants to a different location and by changing parameters such as the composition of the waste input incinerated. The games are performed by using a modular model for municipal solid waste incinerator plants. In each case, an LCA including an Impact Assessment is calculated to trace the impact of these changes, and the results are compared.ConclusionsThe conclusions of the paper are two-fold: (1) reduce the differences in inventory data where their impact on the result is high; where it is possible reducing them to a great extent, and the effort for performing the change acceptable; in the case of incineration plants: Adapt the flue gas treatment, especially a possible DeNOx step, to the real conditions; (2) make use of modular process models that allow adapting plant parameters to better meet real conditions, but be aware of possible modelling errors. The paper invites the scientific community to validate the model used for a waste incinerator plant, and suggest putting up similar models for other processes, preferably those of similar relevance for Life Cycle Inventories.

Development of a methodological framework for social life-cycle assessment of novel technologies

PurposeEnvironmental life-cycle assessment (LCA) is broadly applied and recently social and economic LCA have emerged. However, the development of a general framework for social LCA is still at an early stage of development. The aims of this paper are to systematically discuss general considerations regarding social LCA, to build a consistent and operationalized framework for a number of indicators and to test the framework through application on a case study.MethodsThe first step was to define the scope of the framework starting from a comprehensive review of concepts of social sustainability and social well-being, focusing on the conditions potentially affected by large-scale introduction of novel technologies. Secondly, main areas of concern for social well-being were defined. This resulted in the identification of four main areas of concern. The third step was to make an inventory of potential social indicators and select a number of indicators that could make the framework operational. Additionally, factors for weighting and normalization were developed.Results and discussionThe framework developed in this paper is based on four categories and 11 indicators and follows a life-cycle perspective. Six of the indicators are quantitative and are assessed using an input-output model linked to databases from the International Labour Organization. The remaining five indicators are qualitative indicators which are mapped using expert elicitation and a literature review. Identified concerns regarding the qualitative indicators are “flagged” and provided alongside the results of the quantitative assessment, which are aggregated into one single score by means of a weighted and normalized arithmetical mean. The paper illustrates the application of the methodology in a case study examining the deployment of carbon capture and storage technologies in Europe.ConclusionsThe paper presents a framework that can be used to explore potential impacts on social well-being resulting from the large-scale implementation of novel technologies. The selection of a limited number of indicators (11) keeps the methodology simple and transparent. Although the framework provides a useful approach in allowing both quantitative or qualitative identification of potential areas of concern, the results remain highly explorative in nature. The inherent value-laden and context specific nature of social aspects remains one of the key challenges for developing a general applicable framework.

Geographical and technological differences in Life Cycle Inventories shown by the use of process models for waste incinerators

Goal and BackgroundGeographical and technological differences in Life Cycle Inventory data are an important source for uncertainty in the result of Life Cycle Assessments. Knowledge on their impact on the result of an LCA is scarce, and also knowledge on how to manage them in an LCA case study.ObjectiveGoal of this paper is to explore these differences for municipal solid waste incinerator plants, and to develop recommendations for managing technological and geographical differences.MethodologyThe paper provides a definition of technological and geographical differences, and analyses their possible impacts. In a case study, the differences are caused intentionally in ‘games’, by virtually transplanting incineration plants to a different location and by changing parameters such as the composition of the waste input incinerated. The games are performed by using a modular model for municipal solid waste incinerator plants. In each case, an LCA including an Impact Assessment is calculated to trace the impact of these changes, and the results are compared.ConclusionsThe conclusions of the paper are two-fold: (1) reduce the differences in inventory data where their impact on the result is high; where it is possible reducing them to a great extent, and the effort for performing the change acceptable; in the case of incineration plants: Adapt the flue gas treatment, especially a possible DeNOx step, to the real conditions; (2) make use of modular process models that allow adapting plant parameters to better meet real conditions, but be aware of possible modelling errors. We invite the scientific community to validate the model used for a waste incinerator plant, and suggest putting up similar models for other processes, preferably those of similar relevance for Life Cycle Inventories.

LCA as an element in environmental management systems—comparison of conditions in selected organisations in Poland, Sweden and Germany

PurposeIn this two-part paper (Background and Initial Assumptions (Part 1) and Results of Survey Research (Part 2)), we present surveys whose main objective is to determine, whether and to what extent the life cycle assessment (LCA) technique is used for the identification and assessment of environmental aspects in environmental management systems (EMS) and whether there are any differences in this respect between the companies and countries analysed.MethodsThe survey research was carried out using the computer assisted self-administered interviewing (CASI) method among selected Polish, German and Swedish organisations which implement EMS in accordance with the requirements of ISO 14001 and/or the EMAS regulation.ResultsThe organisations investigated, regardless of their country, are dominated by qualitative and semi-quantitative techniques of assessment and identification of environmental aspects. LCA was used sporadically, although some differences can be observed between the countries analysed.ConclusionsThe environmental managers accustomed to traditional qualitative and semi-quantitative solutions, have not been given preparation to enable them to understand and adopt the different approaches such as LCA. On the other hand, representatives of the organisations investigated declared that they were ready to accept an even longer timescale for the identification and assessment processes relating to environmental aspects, which represents a potential opportunity for LCA. The more precise understanding and definition of environmental problems that are precisely defined in LCA would represent a novelty for environmental managers. In practice, environmental problems are defined in a general sense and rather ambiguously, as this level of detail is sufficient in the context of qualitative and semi-quantitative techniques commonly used for the identification and assessment of environmental aspects.

LCA as an element in environmental management systems—comparison of conditions in selected organisations in Poland, Sweden and Germany Part 1: Background and initial assumptions

PurposeIn this two-part paper (Background and Initial Assumptions (part 1) and Results of Survey Research (part 2)), we present surveys whose main objective is to determine whether, and to what extent, the life cycle assessment (LCA) technique is used for the identification and assessment of environmental aspects in environmental management systems (EMS) and whether there are any differences in this respect between the companies and countries analysed.MethodsThe survey research was carried out using the computer assisted self-administered interviewing method among selected Polish, German and Swedish organisations which implement EMS in accordance with the requirements of ISO 14001 and/or the EMAS regulation.ResultsThe organisations investigated, regardless of their country, are dominated by qualitative and semi-quantitative techniques of assessment and identification of environmental aspects. LCA was used sporadically, although some differences can be observed between the countries analysed.ConclusionsThe environmental managers accustomed to traditional qualitative and semi-quantitative solutions have not been given preparation to enable them to understand and adopt different approaches such as LCA. On the other hand, representatives of the organisations investigated declared that they were ready to accept an even longer timescale for the identification and assessment processes relating to environmental aspects, which represents a potential opportunity for LCA. The more precise understanding and definition of environmental problems that are precisely defined in LCA would represent a novelty for environmental managers. In practice, environmental problems are defined in a general sense and rather ambiguously, as this level of detail is sufficient in the context of qualitative and semi-quantitative techniques commonly used for the identification and assessment of environmental aspects.

A new approach for a modular valuation of LCAs

Goal Scope and BackgroundQualitative valuation methods carefully try to avoid an aggregation across impact categories. However, such an aggregation often helps in obtaining a clear result for the valuation (which product scores better?). This article presents a new valuation method that uses an iterative approach. The application is demonstrated by the help of a case study for electric motors in trains.Methods / Main FeaturesThe approach combines two existing, unique valuation methods described earlier in literature, which both are of a rather non-aggregating nature, in line with ISO requirements, and were designed to be performed by LCA experts. The method is implemented in a computer software. Besides constants used within the method, the software needs as input solely indicator values from the Impact Assessment.Results and DiscussionThe iterative nature of these methods itself, and especially the combination of these methods, helps in achieving a valuation result for the LCA with not more subjective and aggregating elements than necessary. Subjective elements are clearly separated from others. The algorithm seems highly sensitive to changes in impact categories regarded as important ones. The implementation in software greatly eases the application of the method by transferring routine work from LCA experts to a machine. It ensures a reproducible result and prevents erroneous steps in a rather complicated valuation procedure. It further helps in hiding the complexity of the method from the user.ConclusionThe approach of combining valuation methods in LCAs seems a fruitful one, and shows benefits when implemented in computer software, in terms of usability, and in terms of a more reproducible application. Care has to be taken to make sure users know what they do when performing an automated valuation procedure.OutlookWe see three ways for extending the approach, namely: (i) become part of a toolbox of different valuation procedures; (ii) explicitly cope with uncertainty, and (iii) include different values for normalisation, in different regions worldwide. The software will be made available also in a stand alone version.